@Article{Supelec643,
author = {Isidore-Paul Akambita and Michel Barret and Florio Dalla Vedova and Jean-Louis Gutzwiller},
title = {{Lossy and lossless compression of MERIS hyperspectral images with exogenous quasi-optimal spectral transforms}},
journal = {Journal of Applied Remote Sensing},
year = {2010},
volume = {4},
pages = {041790-1-15},
month = {jul},
url = {http://dx.doi.org/10.1117/1.3474980},
doi = {10.1117/1.3474980},
abstract = {Our research focuses on reducing complexity of hyperspectral
image codecs based on transform and/or subband coding, so they
can be on-board a satellite. It is well-known that the Karhunen
Loeve transform (KLT) can be sub-optimal for non Gaussian data.
However, it is generally recommended as the best calculable
coding transform in practice. Now, for a compression scheme
compatible with both the JPEG2000 Part2 standard and the CCSDS
recommendations for onboard satellite image compression, the
concept and computation of optimal spectral transforms (OST), at
high bit-rates, were carried out, under low restrictive
hypotheses. These linear transforms are optimal for reducing
spectral redundancies of multi- or hyper-spectral images, when
the spatial redundancies are reduced with a fixed 2-D discrete
wavelet transform. The problem of OST is their heavy
computational cost. In this paper we present the performances in
coding of a quasi-optimal spectral transform, called exogenous
OrthOST, obtained by learning an orthogonal OST on a sample of
hyperspectral images from the spectrometer MERIS. Moreover, we
compute an integer variant of OrthOST for lossless compression.
The performances are compared to the ones of the KLT in both
lossy and lossless compressions. We observe good performances of
the exogenous OrthOST. }
}